class_docstr: Wrapper around tensorflow optimizer.
class_docstr_tok: ["Wrapper","around","tensorflow","optimizer","."]
docstr: Sets optimizer hyperparams related to entity and relation embeddings (for partitioned training).
  
  Parameters
  ----------
  ent_hyperparams: np.array
      Entity embedding related optimizer hyperparameters.
  rel_hyperparams: np.array
      Relation embedding related optimizer hyperparameters.
docstr_tok: ["Sets","optimizer","hyperparams","related","to","entity","and","relation","embeddings","(","for","partitioned","training",")",".","Parameters","--","--","--","--","--","ent_hyperparams",":","np.array","Entity","embedding","related","optimizer","hyperparameters",".","rel_hyperparams",":","np.array","Relation","embedding","related","optimizer","hyperparameters","."]
code: def set_entity_relation_hyperparams(
      self, ent_hyperparams, rel_hyperparams
  ):
      """Sets optimizer hyperparams related to entity and relation embeddings (for partitioned training).
  
      Parameters
      ----------
      ent_hyperparams: np.array
          Entity embedding related optimizer hyperparameters.
      rel_hyperparams: np.array
          Relation embedding related optimizer hyperparameters.
      """
      optim_weights = self.optimizer.get_weights()
      for i, j in zip(
          range(1, len(optim_weights), self.num_optimized_vars),
          range(len(ent_hyperparams)),
      ):
          optim_weights[i] = ent_hyperparams[j]
          optim_weights[i + 1] = rel_hyperparams[j]
      self.optimizer.set_weights(optim_weights)
  
file_path: AmpliGraph/ampligraph/latent_features/optimizers.py
imports: import abc
  import logging
  import six
  import tensorflow
label_desc: Use image node_id 7 for calling the OptimizerWrapper obj's underlying member method code with example usage: obj.set_entity_relation_hyperparams(ent_hyperparams, rel_hyperparams) without return types